Abstract: Q15.00011 : Statistical laws for career longevity

Authors:

Alexander Petersen
(Boston University)

Woo-Sung Jung
(Boston University)

Jae-Suk Yang
(Korea University)

H. Eugene Stanley
(Boston University)

Career length distinguishes successful long tenures from
unsuccessful short stints, and partially reflects the
contributions of an employee to the goals of the employer.
In some professions, there are well-defined metrics that quantify
career longevity, prowess, and productivity, which
together contribute to the overall success rating for an
individual employee.
In this talk, I motivate a stochastic model for career
development that relies on two key ingredients, random progress
within the career and random stopping times terminating the
career. This model is exactly solvable, predicting the
probability density function (pdf) of career longevity, characterized
by two parameters, $\alpha$ and $x_{c}$. The parameter $\alpha$
quantifies the power-law scaling of the pdf, which is
terminated by an exponential cutoff after a crossover value
$x_{c}$, representing the mean career lifetime.
We test the model with the large quantity of empirical data
available for several professional sports leagues, American
baseball, Korean baseball, American basketball, and English
soccer, finding excellent agreement with the model's predictions.
In all, the generality of the model suggests that there may be
common stochastic forces that underly progress, success, and
longevity in various professions.

To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2009.MAR.Q15.11